code: def plots_multiclass(target, predicted_labels, predicted_probas):
      figures = []
      try:
          #
          fig = plt.figure(figsize=(10, 7))
          ax1 = fig.add_subplot(1, 1, 1)
          _ = skplt.metrics.plot_confusion_matrix(
              target, predicted_labels, normalize=False, ax=ax1
          )
          figures += [
              {
                  "title": "Confusion Matrix",
                  "fname": "confusion_matrix.png",
                  "figure": fig,
              }
          ]
          #
          fig = plt.figure(figsize=(10, 7))
          ax1 = fig.add_subplot(1, 1, 1)
          _ = skplt.metrics.plot_confusion_matrix(
              target, predicted_labels, normalize=True, ax=ax1
          )
          figures += [
              {
                  "title": "Normalized Confusion Matrix",
                  "fname": "confusion_matrix_normalized.png",
                  "figure": fig,
              }
          ]
          #
          fig = plt.figure(figsize=(10, 7))
          ax1 = fig.add_subplot(1, 1, 1)
          _ = skplt.metrics.plot_roc(target, predicted_probas, ax=ax1)
          figures += [
              {
                  "title": "ROC Curve",
                  "fname": "roc_curve.png",
                  "figure": fig,
              }
          ]
          #
          fig = plt.figure(figsize=(10, 7))
          ax1 = fig.add_subplot(1, 1, 1)
          _ = skplt.metrics.plot_precision_recall(
              target, predicted_probas, ax=ax1
          )
          figures += [
              {
                  "title": "Precision Recall Curve",
                  "fname": "precision_recall_curve.png",
                  "figure": fig,
              }
          ]
          plt.close("all")
      except Exception as e:
          print(str(e))
  
      return figures
  
file_path: mljar-supervised/supervised/utils/additional_plots.py
imports: import os
  import numpy
  import pandas
  import scikitplot
  from matplotlib import pyplot
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
label_desc: Use image node_id 2 for calling the AdditionalPlots obj's underlying member method code with example usage: obj.plots_multiclass(target, predicted_labels, predicted_probas) and returns: figures